An artificial intelligence enabled chemical synthesis robot for exploration and optimization of nanomaterials

We present an autonomous chemical synthesis robot for the exploration, discovery, and optimization of nano -structures driven by real-time spectroscopic feedback, theory, and machine learning algorithms that control the reaction conditions and allow the selective templating of reactions. This approach allows the transfer of materials as seeds between cycles of exploration, opening the search space like gene transfer in biology. The open-ended exploration of the seed-mediated multistep synthesis of gold nanoparticles (AuNPs) via in-line ultraviolet-visible characterization led to the discovery of five categories of nanoparticles by only performing ca. 1000 experiments in three hierarchically linked chemical spaces. The platform optimized nanostructures with desired optical properties by combining experiments and extinction spectrum simulations to achieve a yield of up to 95%. The synthetic procedure is outputted in a universal format using the chemical description language (xDL) with analytical data to produce a unique digital signature to enable the reproducibility of the synthesis.

Automated summary

This study presents an autonomous chemical synthesis robot that uses real-time spectroscopic feedback, theory, and machine learning algorithms to explore, discover, and optimize nanostructures. By controlling the reaction conditions and allowing selective templating of reactions, the robot achieves the transfer of materials as seeds between exploration cycles, similar to gene transfer in biology. Through the exploration of seed-mediated multistep synthesis, the robot discovered five categories of nanoparticles by performing only around 1000 experiments in three hierarchically linked chemical spaces. The platform optimizes nanostructures with desired optical properties by combining experiments and extinction spectrum simulations, achieving a yield of up to 95%.